Fine-Grained Natural Language Inference Based Faithfulness Evaluation for Diverse Summarisation Tasks

We study existing approaches to leverage off-the-shelf Natural Language Inference (NLI) models for the evaluation of summary faithfulness and argue that these are sub-optimal due to the granularity level considered for premises and hypotheses. That is, the smaller content unit considered as hypothesis is a sentence and premises are made up of a fixed number of document sentences. We propose a novel approach, namely InFusE, that uses a variable premise size and simplifies summary sentences into shorter hypotheses. Departing from previous studies which focus on single short document summarisation, we analyse NLI based faithfulness evaluation for diverse summarisation tasks. We introduce DiverSumm, a new benchmark comprising long form summarisation (long documents and summaries) and diverse summarisation tasks (e.g., meeting and multi-document summarisation). In experiments, InFusE obtains superior performance across the different summarisation tasks. Our code and data are available at https://github.com/HJZnlp/infuse.Comment: EACL 202

360Roam: Real-Time Indoor Roaming Using Geometry-Aware 360∘^\circ Radiance Fields

Virtual tour among sparse 360$^\circ$ images is widely used while hindering smooth and immersive roaming experiences. The emergence of Neural Radiance Field (NeRF) has showcased significant progress in synthesizing novel views, unlocking the potential for immersive scene exploration. Nevertheless, previous NeRF works primarily focused on object-centric scenarios, resulting in noticeable performance degradation when applied to outward-facing and large-scale scenes due to limitations in scene parameterization. To achieve seamless and real-time indoor roaming, we propose a novel approach using geometry-aware radiance fields with adaptively assigned local radiance fields. Initially, we employ multiple 360$^\circ$ images of an indoor scene to progressively reconstruct explicit geometry in the form of a probabilistic occupancy map, derived from a global omnidirectional radiance field. Subsequently, we assign local radiance fields through an adaptive divide-and-conquer strategy based on the recovered geometry. By incorporating geometry-aware sampling and decomposition of the global radiance field, our system effectively utilizes positional encoding and compact neural networks to enhance rendering quality and speed. Additionally, the extracted floorplan of the scene aids in providing visual guidance, contributing to a realistic roaming experience. To demonstrate the effectiveness of our system, we curated a diverse dataset of 360$^\circ$ images encompassing various real-life scenes, on which we conducted extensive experiments. Quantitative and qualitative comparisons against baseline approaches illustrated the superior performance of our system in large-scale indoor scene roaming

Lightweight, flaw-tolerant, and ultrastrong nanoarchitected carbon

It has been a long-standing challenge in modern material design to create low-density, lightweight materials that are simultaneously robust against defects and can withstand extreme thermomechanical environments, as these properties are often mutually exclusive: The lower the density, the weaker and more fragile the material. Here, we develop a process to create nanoarchitected carbon that can attain specific strength (strength-to-density ratio) up to one to three orders of magnitude above that of existing micro- and nanoarchitected materials. We use two-photon lithography followed by pyrolysis in a vacuum at 900 °C to fabricate pyrolytic carbon in two topologies, octet- and iso-truss, with unit-cell dimensions of ∼2 μm, beam diameters between 261 nm and 679 nm, and densities of 0.24 to 1.0 g/cm^3. Experiments and simulations demonstrate that for densities higher than 0.95 g/cm^3 the nanolattices become insensitive to fabrication-induced defects, allowing them to attain nearly theoretical strength of the constituent material. The combination of high specific strength, low density, and extensive deformability before failure lends such nanoarchitected carbon to being a particularly promising candidate for applications under harsh thermomechanical environments

Moho Depth Variations From Receiver Function Imaging in the Northeastern North China Craton and Its Tectonic Implications

A detailed knowledge of the crustal thickness in the northeastern North China Craton (NCC) is important for understanding the unusual Phanerozoic destruction of the craton. We achieve this goal by employing a 2‐D wave equation‐based migration method to P receiver functions from 198 broadband seismic stations, using Ps conversions and surface‐reflected multiples. By combining receiver function images along 19 profiles, we constructed a high‐resolution Moho depth model for the northeastern NCC. The results present dominant E‐W Moho depth variations similar to previous observations and new regional N‐S variations beneath both sides of the North‐South Gravity Lineament. To the west, while a deeper Moho (∼42 km) appears in the interior of the Trans‐North China Orogen, a relatively shallow Moho (∼38 km) exists in the northern margin of the Trans‐North China Orogen to western NCC. To the east, the crust beneath the Yan Mountains in the marginal area is thicker (∼32–40 km) than that (∼26–32 km) beneath the Bohai Bay Basin in the craton interior, and the Moho further shallows from NE (∼32 km) to SW (∼26 km) within the basin. Along with other observations, we conclude that the dominant E‐W difference may have been associated with the Paleo‐Pacific plate subduction under eastern Asia since the Mesozoic. The newly observed complex N‐S variations may have reflected the structural heterogeneity of the cratonic lithosphere inherited since the formation of the NCC in the Paleoproterozoic, or spatially uneven effects on the cratonic lithosphere of subsequent thermotectonic events during the long‐term evolution of the craton, or both.This research is funded by the National Natural Science Foundation of China (grant 41574034, 41688103, 91414301). Figures are made with GMT (http://gmt.soest.hawaii.edu) and MATLAB softwares (https://www.mathworks.com)

Deformation characteristics of solid-state benzene as a step towards understanding planetary geology

Small organic molecules, like ethane and benzene, are ubiquitous in the atmosphere and surface of Saturn’s largest moon Titan, forming plains, dunes, canyons, and other surface features. Understanding Titan’s dynamic geology and designing future landing missions requires sufficient knowledge of the mechanical characteristics of these solid-state organic minerals, which is currently lacking. To understand the deformation and mechanical properties of a representative solid organic material at space-relevant temperatures, we freeze liquid micro-droplets of benzene to form ~10 μm-tall single-crystalline pyramids and uniaxially compress them in situ. These micromechanical experiments reveal contact pressures decaying from ~2 to ~0.5 GPa after ~1 μm-reduction in pyramid height. The deformation occurs via a series of stochastic (~5-30 nm) displacement bursts, corresponding to densification and stiffening of the compressed material during cyclic loading to progressively higher loads. Molecular dynamics simulations reveal predominantly plastic deformation and densified region formation by the re-orientation and interplanar shear of benzene rings, providing a two-step stiffening mechanism. This work demonstrates the feasibility of in-situ cryogenic nanomechanical characterization of solid organics as a pathway to gain insights into the geophysics of planetary bodies

CoBEV: Elevating Roadside 3D Object Detection with Depth and Height Complementarity

Roadside camera-driven 3D object detection is a crucial task in intelligent transportation systems, which extends the perception range beyond the limitations of vision-centric vehicles and enhances road safety. While previous studies have limitations in using only depth or height information, we find both depth and height matter and they are in fact complementary. The depth feature encompasses precise geometric cues, whereas the height feature is primarily focused on distinguishing between various categories of height intervals, essentially providing semantic context. This insight motivates the development of Complementary-BEV (CoBEV), a novel end-to-end monocular 3D object detection framework that integrates depth and height to construct robust BEV representations. In essence, CoBEV estimates each pixel's depth and height distribution and lifts the camera features into 3D space for lateral fusion using the newly proposed two-stage complementary feature selection (CFS) module. A BEV feature distillation framework is also seamlessly integrated to further enhance the detection accuracy from the prior knowledge of the fusion-modal CoBEV teacher. We conduct extensive experiments on the public 3D detection benchmarks of roadside camera-based DAIR-V2X-I and Rope3D, as well as the private Supremind-Road dataset, demonstrating that CoBEV not only achieves the accuracy of the new state-of-the-art, but also significantly advances the robustness of previous methods in challenging long-distance scenarios and noisy camera disturbance, and enhances generalization by a large margin in heterologous settings with drastic changes in scene and camera parameters. For the first time, the vehicle AP score of a camera model reaches 80% on DAIR-V2X-I in terms of easy mode. The source code will be made publicly available at https://github.com/MasterHow/CoBEV.Comment: The source code will be made publicly available at https://github.com/MasterHow/CoBE